Water Quality Assessment Models for Dokan Lake Using Landsat 8 OLI Satellite Images

Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p less than 0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p less than 0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p less than 0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.

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Histogram based water quality assessment in satellite images

2015 International Conference on Computer and Information Engineering (ICCIE) ◽

10.1109/ccie.2015.7399326 ◽

2015 ◽

Cited By ~ 1

Author(s):

Mumu Aktar ◽

Md. Al Mamun ◽

Md. Shamimur Rahman Shuvo ◽

Md. Ali Hossain

Keyword(s):

Water Quality ◽

Quality Assessment ◽

Satellite Images ◽

Water Quality Assessment

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Water quality assessment in a wetland complex using Sentinel 2 satellite images

International Journal of Environmental Science and Technology ◽

10.1007/s13762-020-02988-3 ◽

2020 ◽

Author(s):

F. Torres-Bejarano ◽

F. Arteaga-Hernández ◽

D. Rodríguez-Ibarra ◽

D. Mejía-Ávila ◽

L. C. González-Márquez

Keyword(s):

Water Quality ◽

Quality Assessment ◽

Satellite Images ◽

Water Quality Assessment ◽

Wetland Complex ◽

Sentinel 2

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Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

Journal of South American Earth Sciences ◽

10.1016/j.jsames.2018.01.004 ◽

2018 ◽

Vol 82 ◽

pp. 231-238 ◽

Cited By ~ 11

Author(s):

Luis Carlos González-Márquez ◽

Franklin M. Torres-Bejarano ◽

Ana Carolina Torregroza-Espinosa ◽

Ivette Renée Hansen-Rodríguez ◽

Hugo B. Rodríguez-Gallegos

Keyword(s):

Water Quality ◽

Quality Assessment ◽

Water Quality Assessment ◽

Landsat 8

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Remote sensing data driven bathing water quality assessment using sentinel-3

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i3.pp1634-1647 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1634

Author(s):

Antonia Senta ◽

Ljiljana Šerić

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Quality Assessment ◽

Satellite Images ◽

Remote Sensing Data ◽

Water Quality Assessment ◽

Data Driven ◽

Bathing Water ◽

Bathing Water Quality

<span>In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period. Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.</span>

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